Data traffic demands and requirements within broadband data communications networks (such as the Internet) are increasing exponentially, and such increases present unique challenges in the associated networking protocols. In order to support such increasing data traffic demands, the network must provide efficient, robust, reliable and flexible services that satisfy the quality of service (QoS) requirements of the underlying applications and services. Further, such network data traffic (e.g., Internet data traffic) involves an increasingly high variety and complexity of data traffic types, such as voice over IP (VOIP), video streaming, interactive data (e.g., web browsing), etc. Such varieties of network data traffic also introduce varied respective functionality and transmission requirements, such as assured latency, minimum throughput levels, security, reliability, privacy, etc. In order to prioritize and satisfy such respective functionality and transmission requirements for the different data types, service provider networks must classify the data transmitted over a network as being associated with the respective data type or service/application type. The network can thereby handle the data of the different classifications appropriately to ensure that the respective functionality and transmission requirements are satisfied (e.g., the required QoS can be preserved for the different traffic types), without applying higher quality levels to data types that do not require such quality levels and thereby providing for efficient use of network resources and maximizing overall network data capacity levels.
Current traffic classification approaches can be basically grouped into three categories: (1) identification of a flow based on IP addresses and port numbers (plus protocol if needed); (2) deep packet inspection (DPI); and (3) inference based on statistics or artificial intelligence. Utilizing the IP and port number classification approach (1) does not necessarily provide for accurate data identification, because the same port may be used for two different data types or classes of data that entail different transmission requirements (e.g., the same port number may be used for web browsing data and video streaming data). For example, port 443 can be used for secure web browsing or secure video download. Further, the DPI approach (2) becomes very limited as the content of more and more Internet data traffic is secured, for example, based on SSL (secured socket layer), IPSec, etc., because the secured data cannot be inspected.
The inference approach (3) is limited based on user behavior and protocols defined by the service providers, which tend to be subjective and time varying leading to only temporarily independent approaches. More generally, a statistical classification method has merit in the sense that it only needs to focus on measurable metrics, such as throughput rate, packet size, session duration, inter-arrival time, etc. The statistical method, however, is still “blind,” resulting in less accurate performance without certain assistance by the network. Further, the common statistical method is also subject to changes in traffic patterns of service provider networks. Accordingly, there are no current statistical approaches that can provide key classification functions with sufficient accuracy.
What is needed, therefore, is an efficient and accurate approach for data traffic classification in broadband data communications networks.